Advancing image segmentation with DBO-Otsu: Addressing rubber tree diseases through enhanced threshold techniques

Addressing the profound impact of Tapping Panel Dryness (TPD) on yield and quality in the global rubber industry, this study introduces a cutting-edge Otsu threshold segmentation technique, enhanced by Dung Beetle Optimization (DBO-Otsu). This innovative approach optimizes the segmentation threshold combination by accelerating convergence and diversifying search methodologies. Following initial segmentation, TPD severity levels are meticulously assessed using morphological characteristics, enabling precise determination of optimal thresholds for final segmentation. The efficacy of DBO-Otsu is rigorously evaluated against mainstream benchmarks like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Feature Similarity Index (FSIM), and compared with six contemporary swarm intelligence algorithms. The findings reveal that DBO-Otsu substantially surpasses its counterparts in image segmentation quality and processing speed. Further empirical analysis on a dataset comprising TPD cases from level 1 to 5 underscores the algorithm’s practical utility, achieving an impressive 80% accuracy in severity level identification and underscoring its potential for TPD image segmentation and recognition tasks.


Introduction:
The introduction section, as it stands, is not satisfactory and should be reviewed again.It lacks the necessary cohesion flow and clarity of ideas.To address these issues, I recommend the following revisions and clarifications: 1. Begin the introduction with a brief overview of the Rubber Tree Top Wilt Disease and how cutting-edge AI techniques and computer vision play a pivotal role in identifying and understanding this disease.This will allow you to smoothly introduce image segmentation as a preprocessing step in medical imaging, setting the foundation for the subsequent discussion.2. The authors mentioned that (traditional Otsu algorithms often face challenges in complex scenarios) Please clearly highlight the limitations of traditional Otsu algorithms in complex scenarios, to underscore the motivation for adopting metaheuristics as an alternative approach.3. Introduce thresholding-based techniques, emphasizing their popularity and relevance as similarity-based methods in the context of your research.4. Provide a more detailed explanation for the selection of Dung Beetle Optimization as the chosen metaheuristic approach and discuss why it was preferred over other available metaheuristics. 5.It is difficult to discern the unique contributions of the proposed model.It would be beneficial to highlight the contributions in bullet points.This would provide readers with a concise and structured overview of the specific advancements by the proposed model.

Inadequate literature review
1. discussion of related works should be expanded with more recent metaheuristics-based image segmentation studies to highlight the limitations of the existing literature, situate the contribution, establish the research gap and highlight the novelty of the proposed approach.2. Some recent related papers that could be included: [ Liu et al., 2021] Liu, L., Zhao, D., Yu, F., Heidari, A. A., Li, C., Ouyang, J., Chen, H., Mafarja, M., Turabieh, H., andPan, J. (2021).Ant colony optimization with cauchy and greedy levy mutations for multilevel covid 19 x-ray image segmentation.Computers in Biology and Medicine, 136:104609.

DBO-Otsu
1. Please expand the DBO-Otsu approach in Section 3, explaining how DBO is integrated with Otsu.Clarify the solution representation and fitness function.Consider using schematic views and visualizations for better clarity.
2. Section 3.1 is a critical component of the manuscript which represent the main contribution.Hence, it is essential to highlight and justify the proposed enhancements.

Experiments and analysis of results
1.It is not clear from the manuscript whether the parameter settings have been tuned specifically for your research or if they are adopted from recommended studies.2. It is important to cite the utilized algorithms, specifying which versions of SSA, CSA, WOA, GWO, WSO, and AHA have been used in this research?3. Please provide a brief description of the rubber dataset used as a benchmark in this study.4. While the selection of three images from the rubber dataset is a good starting point, it's important to note that this sample size may not be sufficient to comprehensively verify the efficiency of the proposed model.It would be beneficial, if possible, to expand the testing to include a more substantial number of images to ensure robust evaluation 5. Please discuss the convergence curves of the proposed approach.
6. Inadequate analysis and discussion of results: The results are not thoroughly analyzed.A more in-depth analysis of the results, including statistical analysis is needed.7. Statistical analysis is important to judge the significance of the findings.

Conclusion:
The conclusion should be explored better and it needs to contemplate the eventual restrictions of the developed technique to address future works in this area.

2) Minor notes:
 Please verify that acronym defini ons are provided upon their ini al use in the manuscript  Please enhance the quality of figures.it would be beneficial to consider conver ng them into vector-based formats  Please check some mistakes: Error! Reference source not found.depicts the movement of rolling dung beetles, For illustra ve purposes, let's assume a total popula on of 30 dung beetles.Using Error!Reference source not found.